Algorithmic Fairness in Canine Behavioral Phenotyping: From Breed Averages to Individual Baselines

Current pet-tech systems rely on opaque scoring mechanisms, activity proxies, and breed-level comparisons that introduce clinically relevant bias when used to infer wellbeing, behavior, or cognitive change. Drawing on human medical AI fairness frameworks — including clinical AI fairness principles (Obermeyer et al., Stanford Medicine) — this paper proposes Barkley's DogGraph architecture as a fairness-by-design framework for canine electronic phenotyping. It maps six principal bias modes — historical, representation, measurement, aggregation, evaluation, and deployment — to their specific manifestations in current pet-tech systems, and describes Barkley's architectural responses: individual-referenced baselines, temporal binning, multimodal signal fusion, Outcome-Action Pairing clinical validation, and natural language abstraction. Barkley does not remove breed, age, or context from the model — it prevents them from becoming the model. Barkley White Paper | Precision Behavioral Intelligence Series | No. 07.